def test_gamma_gamma(self):
x_data = np.array([0.1, 0.5, 3.3, 2.7])
alpha0 = 0.5
beta0 = 1.75
alpha_likelihood = 2.3
beta = rvs.Gamma(alpha0, beta0)
x = rvs.Gamma(alpha_likelihood, beta, sample_shape=4)
beta_cond = ed.complete_conditional(beta, [beta, x])
self.assertIsInstance(beta_cond, rvs.Gamma)
with self.test_session() as sess:
alpha_val, beta_val = sess.run(
[beta_cond.concentration, beta_cond.rate], {x: x_data})
self.assertAllClose(alpha_val, alpha0 + alpha_likelihood * len(x_data))
self.assertAllClose(beta_val, beta0 + x_data.sum())
def test_gamma_exponential(self):
x_data = np.array([0.1, 0.5, 3.3, 2.7])
alpha0 = 0.5
beta0 = 1.75
lam = rvs.Gamma(alpha=alpha0, beta=beta0)
x = rvs.Exponential(lam=lam, sample_shape=4)
lam_cond = ed.complete_conditional(lam, [lam, x])
self.assertIsInstance(lam_cond, rvs.Gamma)
with self.test_session() as sess:
alpha_val, beta_val = sess.run(
[lam_cond.alpha, lam_cond.beta], {x: x_data})
self.assertAllClose(alpha_val, alpha0 + len(x_data))
self.assertAllClose(beta_val, beta0 + x_data.sum())
def test_gamma_poisson(self):
x_data = np.array([0, 1, 0, 7, 0, 0, 2, 0, 0, 1])
alpha0 = 0.5
beta0 = 1.75
lam = rvs.Gamma(alpha=alpha0, beta=beta0)
# use value since cannot sample
x = rvs.Poisson(lam=lam, value=tf.zeros(10), sample_shape=10)
lam_cond = ed.complete_conditional(lam, [lam, x])
self.assertIsInstance(lam_cond, rvs.Gamma)
with self.test_session() as sess:
alpha_val, beta_val = sess.run(
[lam_cond.alpha, lam_cond.beta], {x: x_data})
self.assertAllClose(alpha_val, alpha0 + x_data.sum())
self.assertAllClose(beta_val, beta0 + len(x_data))